PyTorch implementation of UNet++ (Nested U-Net).

Related tags

Deep Learningpytorchimage-segmentationunet
Overview

PyTorch implementation of UNet++ (Nested U-Net)

MIT License

This repository contains code for a image segmentation model based on UNet++: A Nested U-Net Architecture for Medical Image Segmentation implemented in PyTorch.

[NEW] Add support for multi-class segmentation dataset.

[NEW] Add support for PyTorch 1.x.

Requirements

  • PyTorch 1.x or 0.41

Installation

  1. Create an anaconda environment.
conda create -n=<env_name> python=3.6 anaconda
conda activate <env_name>
  1. Install PyTorch.
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
  1. Install pip packages.
pip install -r requirements.txt

Training on 2018 Data Science Bowl dataset

  1. Download dataset from here to inputs/ and unzip. The file structure is the following:
inputs
└── data-science-bowl-2018
    ├── stage1_train
    |   ├── 00ae65...
    │   │   ├── images
    │   │   │   └── 00ae65...
    │   │   └── masks
    │   │       └── 00ae65...            
    │   ├── ...
    |
    ...
  1. Preprocess.
python preprocess_dsb2018.py
  1. Train the model.
python train.py --dataset dsb2018_96 --arch NestedUNet
  1. Evaluate.
python val.py --name dsb2018_96_NestedUNet_woDS

(Optional) Using LovaszHingeLoss

  1. Clone LovaszSoftmax from bermanmaxim/LovaszSoftmax.
git clone https://github.com/bermanmaxim/LovaszSoftmax.git
  1. Train the model with LovaszHingeLoss.
python train.py --dataset dsb2018_96 --arch NestedUNet --loss LovaszHingeLoss

Training on original dataset

Make sure to put the files as the following structure (e.g. the number of classes is 2):

inputs
└── <dataset name>
    ├── images
    |   ├── 0a7e06.jpg
    │   ├── 0aab0a.jpg
    │   ├── 0b1761.jpg
    │   ├── ...
    |
    └── masks
        ├── 0
        |   ├── 0a7e06.png
        |   ├── 0aab0a.png
        |   ├── 0b1761.png
        |   ├── ...
        |
        └── 1
            ├── 0a7e06.png
            ├── 0aab0a.png
            ├── 0b1761.png
            ├── ...
  1. Train the model.
python train.py --dataset <dataset name> --arch NestedUNet --img_ext .jpg --mask_ext .png
  1. Evaluate.
python val.py --name <dataset name>_NestedUNet_woDS

Results

DSB2018 (96x96)

Here is the results on DSB2018 dataset (96x96) with LovaszHingeLoss.

Model IoU Loss
U-Net 0.839 0.365
Nested U-Net 0.842 0.354
Nested U-Net w/ Deepsupervision 0.843 0.362
Owner
4ui_iurz1
4ui_iurz1
GitHub Repository
Learning Tracking Representations via Dual-Branch Fully Transformer Networks

Learning Tracking Representations via Dual-Branch Fully Transformer Networks DualTFR ⭐ We achieves the runner-ups for both VOT2021ST (short-term) and

phiphi 19 May 04, 2022
Parametric Contrastive Learning (ICCV2021)

Parametric-Contrastive-Learning This repository contains the implementation code for ICCV2021 paper: Parametric Contrastive Learning (https://arxiv.or

DV Lab 156 Dec 21, 2022
网络协议2天集训

网络协议2天集训 抓包工具安装 Wireshark wireshark下载地址 Tcpdump CentOS yum install tcpdump -y Ubuntu apt-get install tcpdump -y k8s抓包测试环境 查看虚拟网卡veth pair 查看

120 Dec 12, 2022
Knowledge Management for Humans using Machine Learning & Tags

HyperTag HyperTag helps humans intuitively express how they think about their files using tags and machine learning.

Ravn Tech, Inc. 165 Nov 04, 2022
Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

VQGAN-CLIP-GENERATOR Overview This is a package (with available notebook) for running VQGAN+CLIP locally, with a focus on ease of use, good documentat

Ryan Hamilton 98 Dec 30, 2022
PyTorch code for ICPR 2020 paper Future Urban Scene Generation Through Vehicle Synthesis

Future urban scene generation through vehicle synthesis This repository contains Pytorch code for the ICPR2020 paper "Future Urban Scene Generation Th

Alessandro Simoni 4 Oct 11, 2021
PyTorch code for the ICCV'21 paper: "Always Be Dreaming: A New Approach for Class-Incremental Learning"

Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning PyTorch code for the ICCV 2021 paper: Always Be Dreaming: A New Approach f

49 Dec 21, 2022
Over9000 optimizer

Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC

Mikhail Grankin 405 Nov 27, 2022
Plaything for Autistic Children (demo for PaddlePaddle/Wechaty/Mixlab project)

星星的孩子 - 一款为孤独症孩子设计的聊天机器人游戏 孤独症儿童是目前常常被忽视的一类群体。他们有着类似性格内向的特征，实际却受着广泛性发育障碍的折磨。 项目背景 这类儿童在与人交往时存在着沟通障碍，其特点表现在： 社交交流差，互动障碍明显 认知能力有限，被动认知 兴趣狭窄，重复刻板，缺乏变化和想象

Tianyi Pan 35 Nov 24, 2022
Code for Deep Single-image Portrait Image Relighting

Deep Single-Image Portrait Relighting [Project Page] Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, David W. Jacobs. In ICCV, 2019 Overview Test script for

438 Jan 05, 2023
This is a library for training and applying sparse fine-tunings with torch and transformers.

This is a library for training and applying sparse fine-tunings with torch and transformers. Please refer to our paper Composable Sparse Fine-Tuning f

Cambridge Language Technology Lab 37 Dec 30, 2022
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021]

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps Here is the code for ssbassline model. We also provide OCR results/features/mode

ZephyrZhuQi 51 Nov 18, 2022
Code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search.

TransNAS-Bench-101 This repository contains the publishable code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizabili

Yawen Duan 17 Nov 20, 2022
LieTransformer: Equivariant Self-Attention for Lie Groups

LieTransformer This repository contains the implementation of the LieTransformer used for experiments in the paper LieTransformer: Equivariant Self-At

OxCSML (Oxford Computational Statistics and Machine Learning) 50 Dec 28, 2022
RealTime Emotion Recognizer for Machine Learning Study Jam's demo

Emotion recognizer Table of contents Clone project Dataset Install dependencies Main program Demo 1. Clone project git clone https://github.com/GDSC20

Google Developer Student Club - UIT 1 Oct 05, 2021
Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗

urban_road_filter: a real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles Dependency ROS (tested with Kinetic and

JKK - Vehicle Industry Research Center 180 Dec 12, 2022
Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Hiroshechka Y 33 Dec 26, 2022
OSLO: Open Source framework for Large-scale transformer Optimization

O S L O Open Source framework for Large-scale transformer Optimization What's New: December 21, 2021 Released OSLO 1.0. What is OSLO about? OSLO is a

TUNiB 280 Nov 24, 2022
A real-time motion capture system that estimates poses and global translations using only 6 inertial measurement units

TransPose Code for our SIGGRAPH 2021 paper "TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors". This repository

Xinyu Yi 261 Dec 31, 2022
Byte-based multilingual transformer TTS for low-resource/few-shot language adaptation.

One model to speak them all 🌎 Audio Language Text ▷ Chinese 人人生而自由，在尊严和权利上一律平等。 ▷ English All human beings are born free and equal in dignity and rig

Mutian He 60 Nov 14, 2022